using XSim
using Distributions
using StatsFuns
using Gadfly
using Random
using Printf
using StatsBase
using LinearAlgebra
using JWAS
using DataFrames
using DelimitedFiles
using Cairo
using Fontconfig
using GLM
include("Functions.jl")
SNP_SNP_GWAS2 (generic function with 2 methods)
Random.seed!(95616)
numChr,numLoci,chrLength,mutRate = 1,3020,1.0,2.5e-5 # Mutation rate was 2.5*10−5 during the historical generations
locusInt = chrLength/numLoci
mapPos = collect(0:locusInt:(chrLength-0.0001)) #Uniform position
geneFreq = fill(0.5,numLoci) # initial gene frequency 0.5
XSim.build_genome(numChr,chrLength,numLoci,geneFreq,mapPos,mutRate)
sireSizeFounder = 200
damSizeFounder = 200
sires = sampleFounders(sireSizeFounder)
dams = sampleFounders(damSizeFounder);
Sampling 200 animals into base population. Sampling 200 animals into base population.
ngen,popSize = 500,400
sires1,dams1,gen1 = sampleRan(popSize, ngen, sires, dams);
Generation 2: sampling 200 males and 200 females Generation 3: sampling 200 males and 200 females Generation 4: sampling 200 males and 200 females Generation 5: sampling 200 males and 200 females Generation 6: sampling 200 males and 200 females Generation 7: sampling 200 males and 200 females Generation 8: sampling 200 males and 200 females Generation 9: sampling 200 males and 200 females Generation 10: sampling 200 males and 200 females Generation 11: sampling 200 males and 200 females Generation 12: sampling 200 males and 200 females Generation 13: sampling 200 males and 200 females Generation 14: sampling 200 males and 200 females Generation 15: sampling 200 males and 200 females Generation 16: sampling 200 males and 200 females Generation 17: sampling 200 males and 200 females Generation 18: sampling 200 males and 200 females Generation 19: sampling 200 males and 200 females Generation 20: sampling 200 males and 200 females Generation 21: sampling 200 males and 200 females 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females Generation 102: sampling 200 males and 200 females Generation 103: sampling 200 males and 200 females Generation 104: sampling 200 males and 200 females Generation 105: sampling 200 males and 200 females Generation 106: sampling 200 males and 200 females Generation 107: sampling 200 males and 200 females Generation 108: sampling 200 males and 200 females Generation 109: sampling 200 males and 200 females Generation 110: sampling 200 males and 200 females Generation 111: sampling 200 males and 200 females Generation 112: sampling 200 males and 200 females Generation 113: sampling 200 males and 200 females Generation 114: sampling 200 males and 200 females Generation 115: sampling 200 males and 200 females Generation 116: sampling 200 males and 200 females Generation 117: sampling 200 males and 200 females Generation 118: sampling 200 males and 200 females Generation 119: sampling 200 males and 200 females Generation 120: sampling 200 males and 200 females Generation 121: 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females Generation 141: sampling 200 males and 200 females Generation 142: sampling 200 males and 200 females Generation 143: sampling 200 males and 200 females Generation 144: sampling 200 males and 200 females Generation 145: sampling 200 males and 200 females Generation 146: sampling 200 males and 200 females Generation 147: sampling 200 males and 200 females Generation 148: sampling 200 males and 200 females Generation 149: sampling 200 males and 200 females Generation 150: sampling 200 males and 200 females Generation 151: sampling 200 males and 200 females Generation 152: sampling 200 males and 200 females Generation 153: sampling 200 males and 200 females Generation 154: sampling 200 males and 200 females Generation 155: sampling 200 males and 200 females Generation 156: sampling 200 males and 200 females Generation 157: sampling 200 males and 200 females Generation 158: sampling 200 males and 200 females Generation 159: sampling 200 males and 200 females Generation 160: 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females Generation 297: sampling 200 males and 200 females Generation 298: sampling 200 males and 200 females Generation 299: sampling 200 males and 200 females Generation 300: sampling 200 males and 200 females Generation 301: sampling 200 males and 200 females Generation 302: sampling 200 males and 200 females Generation 303: sampling 200 males and 200 females Generation 304: sampling 200 males and 200 females Generation 305: sampling 200 males and 200 females Generation 306: sampling 200 males and 200 females Generation 307: sampling 200 males and 200 females Generation 308: sampling 200 males and 200 females Generation 309: sampling 200 males and 200 females Generation 310: sampling 200 males and 200 females Generation 311: sampling 200 males and 200 females Generation 312: sampling 200 males and 200 females Generation 313: sampling 200 males and 200 females Generation 314: sampling 200 males and 200 females Generation 315: sampling 200 males and 200 females Generation 316: 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females Generation 336: sampling 200 males and 200 females Generation 337: sampling 200 males and 200 females Generation 338: sampling 200 males and 200 females Generation 339: sampling 200 males and 200 females Generation 340: sampling 200 males and 200 females Generation 341: sampling 200 males and 200 females Generation 342: sampling 200 males and 200 females Generation 343: sampling 200 males and 200 females Generation 344: sampling 200 males and 200 females Generation 345: sampling 200 males and 200 females Generation 346: sampling 200 males and 200 females Generation 347: sampling 200 males and 200 females Generation 348: sampling 200 males and 200 females Generation 349: sampling 200 males and 200 females Generation 350: sampling 200 males and 200 females Generation 351: sampling 200 males and 200 females Generation 352: sampling 200 males and 200 females Generation 353: sampling 200 males and 200 females Generation 354: sampling 200 males and 200 females Generation 355: 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females Generation 453: sampling 200 males and 200 females Generation 454: sampling 200 males and 200 females Generation 455: sampling 200 males and 200 females Generation 456: sampling 200 males and 200 females Generation 457: sampling 200 males and 200 females Generation 458: sampling 200 males and 200 females Generation 459: sampling 200 males and 200 females Generation 460: sampling 200 males and 200 females Generation 461: sampling 200 males and 200 females Generation 462: sampling 200 males and 200 females Generation 463: sampling 200 males and 200 females Generation 464: sampling 200 males and 200 females Generation 465: sampling 200 males and 200 females Generation 466: sampling 200 males and 200 females Generation 467: sampling 200 males and 200 females Generation 468: sampling 200 males and 200 females Generation 469: sampling 200 males and 200 females Generation 470: sampling 200 males and 200 females Generation 471: sampling 200 males and 200 females Generation 472: 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females Generation 492: sampling 200 males and 200 females Generation 493: sampling 200 males and 200 females Generation 494: sampling 200 males and 200 females Generation 495: sampling 200 males and 200 females Generation 496: sampling 200 males and 200 females Generation 497: sampling 200 males and 200 females Generation 498: sampling 200 males and 200 females Generation 499: sampling 200 males and 200 females Generation 500: sampling 200 males and 200 females Generation 501: sampling 200 males and 200 females
ngen,popSize = 1,60
sires1,dams1,gen2 = sampleRan(popSize, ngen, sires1, dams1, gen = gen1);
Generation 502: sampling 30 males and 30 females
ngen,popSize = 1,600
sires1_2,dams1_2,gen2 = sampleRan(popSize, ngen, sires1, dams1, gen = gen1);
Generation 502: sampling 300 males and 300 females
ngen,popSize = 1,1800
siresA1,damsA1,genA1 = sampleRan(popSize, ngen, sires1, dams1_2,gen = gen2)
siresA2,damsA2,genA2 = sampleRan(popSize, ngen, siresA1, damsA1,gen = genA1)
siresA3,damsA3,genA3 = sampleRan(popSize, ngen, siresA2, damsA2,gen = genA2)
siresA4,damsA4,genA4 = sampleRan(popSize, ngen, siresA3, damsA3,gen = genA3)
siresA5,damsA5,genA5 = sampleRan(popSize, ngen, siresA4, damsA4,gen = genA4)
siresA6,damsA6,genA6 = sampleRan(popSize, ngen, siresA5, damsA5,gen = genA5);
Generation 503: sampling 900 males and 900 females Generation 504: sampling 900 males and 900 females Generation 505: sampling 900 males and 900 females Generation 506: sampling 900 males and 900 females Generation 507: sampling 900 males and 900 females Generation 508: sampling 900 males and 900 females
animals4 = concatCohorts(siresA4,damsA4)
animals5 = concatCohorts(siresA5,damsA5)
animals6 = concatCohorts(siresA6,damsA6)
#markers
M4 = getOurGenotypes(animals4)
M5 = getOurGenotypes(animals5)
M6 = getOurGenotypes(animals6)
M = [
M4
M5
M6
]
#pedigrees
pedigree4 = getPedigree(animals4)
pedigree5 = getPedigree(animals5)
pedigree6 = getPedigree(animals6)
pedigree = [
pedigree4
pedigree5
pedigree6
];
k = size(M,2)
QTLa = 20 # AddQTL
QTLd = 6 # Dominant SNP
SNPpairs = 3 # SNP–SNP pairs
QTLPos = sample(1:k,QTLa,replace=false)
mrkPos = deleteat!(collect(1:k),sort(QTLPos))
Qa = M[:,sort(QTLPos)]
X = M[:,mrkPos]
newQTLPos = sort(QTLPos) .- (1:QTLa) # Position of QTL in marker matrix
@printf "The number of additive QTL = %5i \n" nQTL = size(Qa,2)
@printf "The number of dominant QTL = %5i \n" QTLd
@printf "The number of epistatic pairs = %5i \n" SNPpairs
@printf "The maximum MAF = %.2f \n" maximum(maf(X))
@printf "The number of markers = %5i \n" nMarkers = size(X,2)
@printf "The number of phenotypic observations = %5i \n" nObs = size(Qa,1)
The number of additive QTL = 20 The number of dominant QTL = 6 The number of epistatic pairs = 3 The maximum MAF = 0.50 The number of markers = 3000 The number of phenotypic observations = 5400
Xnew = maf(X)
sel1 = vec(Xnew .>= 0.01)
Marker1 = X[:,sel1]
sel2 = vec(Xnew .>= 0.02)
Marker2 = X[:,sel2]
sel3 = vec(Xnew .>= 0.05)
Marker3 = X[:,sel3];
corMat = cor(Marker1)
LDMat = zeros(1800,200)
for i = 1:1800
LDMat[i,:] = corMat[i,(i+1):(i+200)].^2
end
replace!(LDMat, NaN => 0)
y_Axis = mean(LDMat,dims=1)
plot(x=0.05:0.05:1.0,y=y_Axis[1:20],Guide.title("LD decreases with distance in the first 1cM, MAF>=0.01"), Guide.xlabel("Map Distance，cM"), Guide.ylabel("LD"),Theme(default_color=colorant"blue"))
corMat = cor(Marker2)
LDMat = zeros(1800,200)
for i = 1:1800
LDMat[i,:] = corMat[i,(i+1):(i+200)].^2
end
replace!(LDMat, NaN => 0)
y_Axis = mean(LDMat,dims=1)
plot(x=0.05:0.05:1.0,y=y_Axis[1:20],Guide.title("LD decreases with distance in the first 1cM, MAF>=0.02"), Guide.xlabel("Map Distance，cM"), Guide.ylabel("LD"),Theme(default_color=colorant"blue"))
corMat = cor(Marker3)
LDMat = zeros(1800,200)
for i = 1:1800
LDMat[i,:] = corMat[i,(i+1):(i+200)].^2
end
replace!(LDMat, NaN => 0)
y_Axis = mean(LDMat,dims=1)
plot(x=0.05:0.05:1.0,y=y_Axis[1:20],Guide.title("LD decreases with distance in the first 1cM, MAF>=0.05"), Guide.xlabel("Map Distance，cM"), Guide.ylabel("LD"),Theme(default_color=colorant"blue"))
freq = mean(X,dims=1)/2
plot(x=freq,Geom.histogram,
Guide.title("Distribution of Gene Frequency"),
Guide.ylabel("Frequency"),
Guide.xlabel("Gene Frequency"))
meanXCols = mean(X,dims = 1)
p = meanXCols./2
@printf "The average frequency of heterozygote = %.2f \n" mean2pq = mean(2p.*(1 .- p))
@printf "The average frequency of p = %.2f \n" mean_p = mean(p.^2)
@printf "The average frequency of q = %.2f \n" mean_q = mean((1 .- p).^2)
@printf "Sum of three genotype frequencies = %.2f \n" mean_p + mean2pq + mean_q
The average frequency of heterozygote = 0.26 The average frequency of p = 0.37 The average frequency of q = 0.37 Sum of three genotype frequencies = 1.00
sumXCols = sum(X,dims = 1)
geno2sameColnum = size(findall( x -> x == nObs*2, sumXCols))[1]
geno0sameColnum = size(findall( x -> x == 0, sumXCols))[1]
allmumFixLoc = geno2sameColnum + geno0sameColnum
@printf "The number of fixed loci that genotypes are 2 = %5i \n" geno2sameColnum
@printf "The number of fixed loci that genotypes are 0 = %5i \n" geno0sameColnum
@printf "Total number of fixed loci = %5i \n" allmumFixLoc
The number of fixed loci that genotypes are 2 = 236 The number of fixed loci that genotypes are 0 = 233 Total number of fixed loci = 469
α = rand(Gamma(0.4),QTLa) .* sample([-1,1],QTLa); #scaling parameter α and shape parameter β
d, SortQTLDompos, SortQTLpos = Dominant(QTLPos,QTLd,α);
epistaticQTLPos, epi_coef, epi_combinations = epistatic(QTLPos, SNPpairs);
p = getaf(Qa)
#set fixed allele frequencies to an extreme frequency, so that the algorithm in epi() still works for all loci
p[ p .== 1.0 ] .= 0.99999
p[ p .== 0.0 ] .= 0.00001
#compute genotype frequencies from random mating
gfreq = gf(p,p)
#get epistatic effects
epimodel = "interaction"
epi_vars, epi_effects = epi(gfreq, p, epi_coef, α, epimodel, epi_combinations);
#get alphas and ds
Newd = zeros(nQTL)
DominQTLpos = findall(x->x in SortQTLDompos, SortQTLpos)
Newd[DominQTLpos] .= d
alphas, ds = get_effects(α,Newd,p,epi_combinations, epi_effects);
Computing orthogonal alphas and ds
#compute noia matrices
noia, noia_dom = make_noia(Qa);
Creating NOIA matrices.. Done creating NOIA matrices!
#breeding values and dominance deviations
simbv = noia * alphas
simdom = noia_dom * ds;
aa = epistaticCom(noia, epi_combinations, noia_dom, nObs, "interaction");
simtgv = simbv + simdom + aa
#phenotypes
resVar = var(simtgv)
resid = rand(Normal(0,sqrt(resVar)), nObs)
pheno = simtgv .+ resid;
h2N = var(simbv)/var(pheno)
0.5029665893829274
h2B = var(simtgv)/var(pheno)
0.5089043058964029
QTLPosIndex = [SortQTLpos newQTLPos]
SortQTLDompos = TruePosDom(SortQTLDompos,QTLPosIndex)
sortepistaticQTLPos = TruePosEpis(epistaticQTLPos,QTLPosIndex);
M = Float64.(X)
p = getaf(M)
p[ p .== 1.0 ] .= 0.99999
p[ p .== 0.0 ] .= 0.00001
C = findall(p .< 0.5)
p[C] .= 1 .- p[C]
Z, W = Zmatrix(M, p);
Ka = Z * Z'
Kd = W * W'
Ki = Ka .* Ka - (Z .* Z)*(Z .* Z)';
GA = (Z'*Z + I*0.00001)./(2*sum(p .*(1 .- p)))
GD = (W'*W + I*0.00001)./(4*sum(p.^2 .* (1 .- p).^2));
GAAtem = GA .* GA + I*0.00001
n = size(GAAtem)[2]
GAA = GAAtem ./ (tr(GAAtem) / n);
ids = string.(1:size(M,1))
model = build_model("y = intercept",resVar)
add_genotypes(model,M,var(simbv),header=false,rowID=ids,G_is_marker_variance=false);
The marker IDs are set to 1,2,...,#markers
#markers: 3000; #individuals: 5400
phenTrain = DataFrame(ids=1:size(pheno,1), y=pheno)
out=runMCMC(model, phenTrain, # tell JWAS to run analysis, for given model and data
Pi=0.99, # intial value of π
estimatePi=true,
chain_length=60000, # length of chain
printout_frequency=5000, # how often to show progress of analysis
printout_model_info=true, # tell JWAS to show the options used in this analysis
methods="BayesC", # tell JWAS to run a BayesC analysis
output_samples_frequency=20, # how often to output sampled quantities
output_folder ="results",
output_samples_for_all_parameters = true,
outputEBV=true
);
The folder results is created to save results. Checking phenotypes... Individual IDs (strings) are provided in the first column of the phenotypic data. The number of observations with both genotypes and phenotypes used in the analysis is 5400. The prior for marker effects variance is calculated from the genetic variance and π. The mean of the prior for the marker effects variance is: 1.61822 A Linear Mixed Model was build using model equations: y = intercept Model Information: Term C/F F/R nLevels intercept factor fixed 1 MCMC Information: chain_length 60000 burnin 0 starting_value true printout_frequency 5000 output_samples_frequency 20 constraint false missing_phenotypes true update_priors_frequency 0 seed false Hyper-parameters Information: residual variances: 12.860 Genomic Information: complete genomic data (i.e., non-single-step analysis) Genomic Category geno Method BayesC genetic variances (genomic): 12.710 marker effect variances: 1.618 π 0.99 estimatePi true estimateScale false Degree of freedom for hyper-parameters: residual variances: 4.000 marker effect variances: 4.000 The file results/MCMC_samples_residual_variance.txt is created to save MCMC samples for residual_variance. The file results/MCMC_samples_marker_effects_geno_y.txt is created to save MCMC samples for marker_effects_geno_y. The file results/MCMC_samples_marker_effects_variances_geno.txt is created to save MCMC samples for marker_effects_variances_geno. The file results/MCMC_samples_pi_geno.txt is created to save MCMC samples for pi_geno. The file results/MCMC_samples_y.intercept.txt is created to save MCMC samples for y:intercept. The file results/MCMC_samples_EBV_y.txt is created to save MCMC samples for EBV_y. The file results/MCMC_samples_genetic_variance.txt is created to save MCMC samples for genetic_variance.
running MCMC ... 0%|█ | ETA: 0:04:23
The file results/MCMC_samples_heritability.txt is created to save MCMC samples for heritability.
running MCMC ... 8%|███ | ETA: 0:04:26
Posterior means at iteration: 5000 Residual variance: 13.109601
running MCMC ... 17%|██████ | ETA: 0:03:55
Posterior means at iteration: 10000 Residual variance: 13.107325
running MCMC ... 17%|██████ | ETA: 0:03:55
running MCMC ... 25%|█████████ | ETA: 0:03:28
Posterior means at iteration: 15000 Residual variance: 13.108311
running MCMC ... 25%|█████████ | ETA: 0:03:28
running MCMC ... 33%|████████████ | ETA: 0:03:01
Posterior means at iteration: 20000 Residual variance: 13.109566
running MCMC ... 33%|████████████ | ETA: 0:03:01
running MCMC ... 42%|███████████████ | ETA: 0:02:38
Posterior means at iteration: 25000 Residual variance: 13.112109
running MCMC ... 42%|███████████████ | ETA: 0:02:38
running MCMC ... 50%|██████████████████ | ETA: 0:02:16
Posterior means at iteration: 30000 Residual variance: 13.11568
running MCMC ... 50%|██████████████████ | ETA: 0:02:16
running MCMC ... 58%|█████████████████████ | ETA: 0:01:53
Posterior means at iteration: 35000 Residual variance: 13.121654
running MCMC ... 58%|█████████████████████ | ETA: 0:01:53
running MCMC ... 67%|████████████████████████ | ETA: 0:01:30
Posterior means at iteration: 40000 Residual variance: 13.12146
running MCMC ... 67%|████████████████████████ | ETA: 0:01:30
running MCMC ... 75%|███████████████████████████ | ETA: 0:01:08
Posterior means at iteration: 45000 Residual variance: 13.120212
running MCMC ... 83%|██████████████████████████████ | ETA: 0:00:45
Posterior means at iteration: 50000 Residual variance: 13.117911
running MCMC ... 92%|█████████████████████████████████ | ETA: 0:00:23
Posterior means at iteration: 55000 Residual variance: 13.116843
running MCMC ... 100%|███████████████████████████████████| ETA: 0:00:00
Posterior means at iteration: 60000 Residual variance: 13.115795
running MCMC ... 100%|███████████████████████████████████| Time: 0:04:30
The version of Julia and Platform in use: Julia Version 1.8.3 Commit 0434deb161 (2022-11-14 20:14 UTC) Platform Info: OS: Windows (x86_64-w64-mingw32) CPU: 4 × Intel(R) Core(TM) i7-7500U CPU @ 2.70GHz WORD_SIZE: 64 LIBM: libopenlibm LLVM: libLLVM-13.0.1 (ORCJIT, skylake) Threads: 1 on 4 virtual cores The analysis has finished. Results are saved in the returned variable and text files. MCMC samples are saved in text files.
res = GWAS("results/MCMC_samples_marker_effects_geno_y.txt";header=true);
Compute the model frequency for each marker (the probability the marker is included in the model).
maximum(newQTLPos)
2792
res[newQTLPos,:]
| marker_ID | modelfrequency | |
|---|---|---|
| Abstrac… | Float64 | |
| 1 | 263 | 0.003 |
| 2 | 285 | 0.000333333 |
| 3 | 370 | 0.00366667 |
| 4 | 371 | 0.00833333 |
| 5 | 424 | 0.0 |
| 6 | 449 | 0.0 |
| 7 | 656 | 0.00166667 |
| 8 | 688 | 0.0 |
| 9 | 751 | 0.0 |
| 10 | 990 | 0.00366667 |
| 11 | 1026 | 0.0 |
| 12 | 1135 | 0.000333333 |
| 13 | 1525 | 0.424333 |
| 14 | 2117 | 0.0 |
| 15 | 2248 | 1.0 |
| 16 | 2292 | 0.0 |
| 17 | 2480 | 0.00233333 |
| 18 | 2603 | 0.0 |
| 19 | 2678 | 0.000333333 |
| 20 | 2792 | 0.00433333 |
out["EBV_y"]
| ID | EBV | PEV | |
|---|---|---|---|
| Any | Any | Any | |
| 1 | 1 | -3.75168 | 0.0668002 |
| 2 | 2 | 1.08018 | 0.0459823 |
| 3 | 3 | -3.94868 | 0.0943946 |
| 4 | 4 | -0.339452 | 0.0350379 |
| 5 | 5 | -5.47086 | 0.0522717 |
| 6 | 6 | 0.823386 | 0.0587298 |
| 7 | 7 | -4.19625 | 0.126959 |
| 8 | 8 | 0.755664 | 0.0350884 |
| 9 | 9 | -4.16374 | 0.0548528 |
| 10 | 10 | 0.784702 | 0.125299 |
| 11 | 11 | -3.61296 | 0.104193 |
| 12 | 12 | 4.12392 | 0.0440072 |
| 13 | 13 | -0.781203 | 0.0542116 |
| 14 | 14 | 0.454696 | 0.0342249 |
| 15 | 15 | -4.40029 | 0.0697592 |
| 16 | 16 | -0.389248 | 0.0649443 |
| 17 | 17 | 0.482767 | 0.0488711 |
| 18 | 18 | 6.03878 | 0.0732939 |
| 19 | 19 | 0.777165 | 0.0355207 |
| 20 | 20 | -1.35145 | 0.0678147 |
| 21 | 21 | -6.3602 | 0.0535102 |
| 22 | 22 | 0.162739 | 0.0449542 |
| 23 | 23 | -3.88393 | 0.0483218 |
| 24 | 24 | -4.07993 | 0.100401 |
| 25 | 25 | 0.469209 | 0.0329794 |
| 26 | 26 | -0.789776 | 0.0766673 |
| 27 | 27 | -5.4987 | 0.0405693 |
| 28 | 28 | -5.48118 | 0.0987457 |
| 29 | 29 | -4.36224 | 0.0652698 |
| 30 | 30 | -4.23726 | 0.0372082 |
| ⋮ | ⋮ | ⋮ | ⋮ |
winVar = GWAS("results/MCMC_samples_marker_effects_geno_y.txt",M;header=true,window_size=150,threshold=0.001)
This function is deprecated. Please check ?GWAS
| wStart | wEnd | wSize | prGenVar | WPPA | PPA_t | |
|---|---|---|---|---|---|---|
| Int64 | Int64 | Int64 | Float64 | Float64 | Float64 | |
| 1 | 301 | 450 | 150 | 4.01 | 1.0 | 1.0 |
| 2 | 2101 | 2250 | 150 | 101.88 | 1.0 | 1.0 |
| 3 | 2251 | 2400 | 150 | 4.52 | 1.0 | 1.0 |
| 4 | 1951 | 2100 | 150 | 0.77 | 0.999667 | 0.999917 |
| 5 | 1501 | 1650 | 150 | 0.44 | 0.931667 | 0.986267 |
| 6 | 2551 | 2700 | 150 | 0.34 | 0.802667 | 0.955667 |
| 7 | 2701 | 2850 | 150 | 0.05 | 0.165 | 0.842714 |
| 8 | 1351 | 1500 | 150 | 0.02 | 0.0686667 | 0.745958 |
| 9 | 2851 | 3000 | 150 | 0.02 | 0.0636667 | 0.670148 |
| 10 | 901 | 1050 | 150 | 0.02 | 0.0606667 | 0.6092 |
| 11 | 2401 | 2550 | 150 | 0.02 | 0.0476667 | 0.558152 |
| 12 | 1651 | 1800 | 150 | 0.01 | 0.0373333 | 0.51475 |
| 13 | 601 | 750 | 150 | 0.01 | 0.0333333 | 0.477718 |
| 14 | 1201 | 1350 | 150 | 0.01 | 0.0223333 | 0.44519 |
| 15 | 451 | 600 | 150 | 0.0 | 0.0163333 | 0.4166 |
| 16 | 151 | 300 | 150 | 0.0 | 0.0123333 | 0.391333 |
| 17 | 1 | 150 | 150 | 0.0 | 0.007 | 0.368725 |
| 18 | 751 | 900 | 150 | 0.0 | 0.007 | 0.34863 |
| 19 | 1801 | 1950 | 150 | 0.0 | 0.00533333 | 0.330561 |
| 20 | 1051 | 1200 | 150 | 0.0 | 0.00366667 | 0.314217 |
srtIndx = sortperm(winVar.wStart,rev=false)
winVar = DataFrame(wStart = winVar.wStart[srtIndx],
wEnd = winVar.wEnd[srtIndx],
wSize = winVar.wSize[srtIndx],
prGenVar = winVar.prGenVar[srtIndx],
WPPA = winVar.WPPA[srtIndx],
PPA_t = winVar.PPA_t[srtIndx]
)
| wStart | wEnd | wSize | prGenVar | WPPA | PPA_t | |
|---|---|---|---|---|---|---|
| Int64 | Int64 | Int64 | Float64 | Float64 | Float64 | |
| 1 | 1 | 150 | 150 | 0.0 | 0.007 | 0.368725 |
| 2 | 151 | 300 | 150 | 0.0 | 0.0123333 | 0.391333 |
| 3 | 301 | 450 | 150 | 4.01 | 1.0 | 1.0 |
| 4 | 451 | 600 | 150 | 0.0 | 0.0163333 | 0.4166 |
| 5 | 601 | 750 | 150 | 0.01 | 0.0333333 | 0.477718 |
| 6 | 751 | 900 | 150 | 0.0 | 0.007 | 0.34863 |
| 7 | 901 | 1050 | 150 | 0.02 | 0.0606667 | 0.6092 |
| 8 | 1051 | 1200 | 150 | 0.0 | 0.00366667 | 0.314217 |
| 9 | 1201 | 1350 | 150 | 0.01 | 0.0223333 | 0.44519 |
| 10 | 1351 | 1500 | 150 | 0.02 | 0.0686667 | 0.745958 |
| 11 | 1501 | 1650 | 150 | 0.44 | 0.931667 | 0.986267 |
| 12 | 1651 | 1800 | 150 | 0.01 | 0.0373333 | 0.51475 |
| 13 | 1801 | 1950 | 150 | 0.0 | 0.00533333 | 0.330561 |
| 14 | 1951 | 2100 | 150 | 0.77 | 0.999667 | 0.999917 |
| 15 | 2101 | 2250 | 150 | 101.88 | 1.0 | 1.0 |
| 16 | 2251 | 2400 | 150 | 4.52 | 1.0 | 1.0 |
| 17 | 2401 | 2550 | 150 | 0.02 | 0.0476667 | 0.558152 |
| 18 | 2551 | 2700 | 150 | 0.34 | 0.802667 | 0.955667 |
| 19 | 2701 | 2850 | 150 | 0.05 | 0.165 | 0.842714 |
| 20 | 2851 | 3000 | 150 | 0.02 | 0.0636667 | 0.670148 |
nWins = size(winVar,1)
some_plot1 =plot(x=(1:nWins),y=winVar.WPPA,Guide.title("WPPA of genome windows"), Guide.xlabel("Genomic windows"), Guide.ylabel("WPPA"),
Theme(default_color=colorant"blue"))
markerEffect=out["marker effects geno"]
add = sortslices([newQTLPos/((numLoci*numChr)-nQTL)*nWins abs.(alphas)],dims=1,by=x->x[1],rev=false)
some_plot2 =plot(layer(x=(1:size(markerEffect,1))*nWins/((numLoci*numChr)-nQTL),y=broadcast(abs,markerEffect.Estimate), Geom.point, Theme(default_color=colorant"blue")),Guide.title("Additive effect SNP scan results"), Guide.xlabel("Genomic windows"), Guide.ylabel("Marker effect"),
layer(x=add[:,1], y=broadcast(abs, add[:,2]), Geom.point, Theme(default_color=colorant"red")))
DominantLoc = SortQTLDompos
dominant = sortslices([DominantLoc/((numLoci*numChr)-nQTL)*nWins abs.(ds[DominQTLpos])],dims=1,by=x->x[1],rev=false)
some_plot3 =plot(layer(x=(1:size(markerEffect,1))*nWins/((numLoci*numChr)-nQTL),y=broadcast(abs,markerEffect.Estimate), Geom.point, Theme(default_color=colorant"blue")),Guide.title("Dominant effect SNP scan results"), Guide.xlabel("Genomic windows"), Guide.ylabel("Marker effect"),
layer(x=dominant[:,1], y=broadcast(abs, dominant[:,2]), Geom.point, Theme(default_color=colorant"red")))
episLocmean = TruePos(epi_combinations,QTLPosIndex)
epistasis = sortslices([episLocmean/((numLoci*numChr)-nQTL)*nWins abs.(sum(epi_effects[:,[5]],dims=2))],dims=1,by=x->x[1],rev=false)
some_plot4 =plot(layer(x=(1:size(markerEffect,1))*nWins/((numLoci*numChr)-nQTL),y=broadcast(abs,markerEffect.Estimate), Geom.point, Theme(default_color=colorant"blue")),Guide.title("Epistatic effect SNP scan results"), Guide.xlabel("Genomic windows"), Guide.ylabel("Marker effect"),
layer(x=epistasis[:,1], y=broadcast(abs, epistasis[:,2]), Geom.point, Theme(default_color=colorant"red")))
out["marker effects geno"]
| Trait | Marker_ID | Estimate | SD | Model_Frequency | |
|---|---|---|---|---|---|
| Any | Any | Any | Any | Any | |
| 1 | y | 1 | 0.0 | 0.0 | 0.0 |
| 2 | y | 2 | 1.28741f-5 | 0.000705023 | 0.000333334 |
| 3 | y | 3 | 0.0 | 0.0 | 0.0 |
| 4 | y | 4 | 0.0 | 0.0 | 0.0 |
| 5 | y | 5 | 0.0 | 0.0 | 0.0 |
| 6 | y | 6 | 0.0 | 0.0 | 0.0 |
| 7 | y | 7 | -0.00500998 | 0.154723 | 0.00366667 |
| 8 | y | 8 | 0.0 | 0.0 | 0.0 |
| 9 | y | 9 | -4.83103f-5 | 0.00188028 | 0.000666667 |
| 10 | y | 10 | 0.000443706 | 0.138407 | 0.00366667 |
| 11 | y | 11 | -0.000115906 | 0.00397087 | 0.001 |
| 12 | y | 12 | 0.0 | 0.0 | 0.0 |
| 13 | y | 13 | 0.0 | 0.0 | 0.0 |
| 14 | y | 14 | 0.000114146 | 0.0045643 | 0.000666668 |
| 15 | y | 15 | 0.0 | 0.0 | 0.0 |
| 16 | y | 16 | 0.00378067 | 0.0593366 | 0.00466667 |
| 17 | y | 17 | 0.0 | 0.0 | 0.0 |
| 18 | y | 18 | -7.0054f-5 | 0.00383638 | 0.000333333 |
| 19 | y | 19 | 0.0 | 0.0 | 0.0 |
| 20 | y | 20 | 0.00271404 | 0.149564 | 0.00533334 |
| 21 | y | 21 | 0.0 | 0.0 | 0.0 |
| 22 | y | 22 | 0.000167374 | 0.00571394 | 0.001 |
| 23 | y | 23 | 0.000197619 | 0.00644286 | 0.001 |
| 24 | y | 24 | 0.000307976 | 0.00986087 | 0.001 |
| 25 | y | 25 | 0.0 | 0.0 | 0.0 |
| 26 | y | 26 | 0.000165046 | 0.147904 | 0.00333333 |
| 27 | y | 27 | 0.0 | 0.0 | 0.0 |
| 28 | y | 28 | 0.0 | 0.0 | 0.0 |
| 29 | y | 29 | 3.1048f-5 | 0.00170028 | 0.000333334 |
| 30 | y | 30 | 0.00202614 | 0.121984 | 0.00266667 |
| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |
wSize = 150
wStartV,wEndV,testStat = winTest(M[1:500,:],pheno[1:500],var(simbv),resVar,wSize)
srtIndx = sortperm(testStat,rev=true)
replace!(testStat, NaN => 0)
bigF = [wStartV[srtIndx] wEndV[srtIndx] testStat[srtIndx]];
some_plot5 =plot(x=(1:nWins),y=testStat,Guide.title("F statistics of Genome window "), Guide.xlabel("Genomic windows"), Guide.ylabel("F value"),
Theme(default_color=colorant"blue"))
sortPosQTL = sort(QTLPos)
wPos = [bigF [findfirst(bigF[i,1] .<= sortPosQTL .< bigF[i,2]) for i=1:size(bigF,1)]]
20×4 Matrix{Union{Nothing, Float64}}:
1051.0 1200.0 6.50106 12.0
2551.0 2700.0 5.35068 18.0
2851.0 3000.0 5.23125 nothing
1801.0 1950.0 5.18532 nothing
2701.0 2850.0 4.86845 20.0
1951.0 2100.0 4.73664 nothing
301.0 450.0 4.70326 3.0
1201.0 1350.0 4.63476 nothing
1501.0 1650.0 4.63083 13.0
601.0 750.0 4.62811 7.0
151.0 300.0 4.62026 1.0
901.0 1050.0 4.2378 10.0
2101.0 2250.0 4.17256 14.0
1.0 150.0 4.04736 nothing
1351.0 1500.0 3.90571 nothing
751.0 900.0 3.87372 9.0
2401.0 2550.0 3.78504 17.0
2251.0 2400.0 3.69143 15.0
451.0 600.0 3.52503 6.0
1651.0 1800.0 3.2208 nothing